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Last week I received the result
of my WSET level 3.
I got a pass with distinction
for both parts of the exam,
the tasting
and the theory.
So, keeping up the tradition
I started with level 2,
this post summarises my advice
for anyone out there
thinking about level 3.
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Automating your release process
saves time,
eliminates tedious busywork
and reduces the likelihood of mistakes
when cutting a new release.
There are plenty of off-the-shelf solutions available,
but this post will show
how easy it is to build your own release script
and why the end result can be better
than using a generic, third-party option.
Throughout the post
I'll use the case study
of some recent work we did
to automate the release process
for FxA,
to provide concrete examples
of what I'm talking about.
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Historically,
the Firefox Accounts (FxA) codebase
was organised as separate repositories
because it's deployed as separate microservices
in production.
However,
that arrangement caused us
some nagging issues
as developers,
so we decided to migrate the code
to a monorepo instead.
This post discusses
why and how we did that,
and how things turned out
in the end.
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2019 will be a big year for Firefox Accounts,
with it forming the cornerstone
of Mozilla's plan for growth as a platform.
So we're hiring right now
for the position of
Senior Software Engineer on the FxA team.
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Along with all the fun, creative stuff
it enables you to do,
programming sometimes requires you to carry out
boring and repetitive editing operations.
If those operations are uniformly applicable,
it's straightforward to automate them
using regular expressions
and a tool like sed,
or :%s/foo/bar/g in vim-speak.
But sometimes a regex can't express
the pattern you want to match against
and on those occasions,
vim macros can come to the rescue.
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JavaScript's Array and Promise types
compose nicely
with functional programming idioms
to control concurrency
without recourse to 3rd-party libraries.
This post contrasts two such patterns
that enable you to process data
either concurrently or serially,
with back-off and retry logic
in the event of errors occurring.
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Time passes
and it's easy to forget
about all the cool stuff you did
or lessons you learned.
But source control makes it easy
to go back and remind yourself,
so I thought I'd take a moment
to look back and summarise
my past year in code.
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For Christmas I bought myself a Garmin Forerunner 645,
after becoming irritated to the point of distraction
by my Polar Flow M430.
Now that I've used the Garmin
for a few weeks,
this is my compare-and-contrast review
of the two devices.
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Recently I sat my WSET level 2
and did pretty well.
I scored 90% in the exam,
which is a pass with distinction.
Based on that experience,
these are my tips
for anyone else planning
to take the course.
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About a month ago,
we had an outage
caused by a slow-running query in MySQL.
This particular slow query
wasn't spotted when it was deployed
because it depended on data
inserted by client browsers
and the related preference in Firefox
was not enabled at that point.
A few weeks after it shipped,
the client pref was flipped on
and as the table grew,
it slowed down MySQL increasingly
until the whole of Firefox Accounts
became unresponsive.
And of course,
because Sod's Law
is one of the fundamental forces of nature,
this happened late on a Friday night.
There were some complicating factors
that slowed down diagnosis,
but it's also fair to say
we could have caught it at source
with an EXPLAIN of the offending query
during code review.
Because of that,
I decided to try and automate
EXPLAIN checks for our MySQL queries.
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Refactoring boilerplate code is always easy
in dynamically-typed languages,
but sometimes takes a bit more effort
when constrained by strong typing.
This is something I was puzzling over recently,
when the penny dropped for me
about how Rust's macros
can be used to bridge the gap.
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Let's say you have a huge amount of JSON data
and you want to parse values from it in Node.js.
Perhaps it's stored in a file on disk
or, more trickily,
it's on a remote machine
and you don't want to download the entire thing
just to get some data from it.
And even if it is on the local file system,
the thing is so huge that reading it in to memory
and calling JSON.parse
will crash the process
with an out-of-memory exception.
Today I implemented a new method
for my async JSON-parsing lib, BFJ,
which has exactly this type of scenario in mind.
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"Bad performance hurts user engagement"
is a sentiment that feels intuitively true
but can be hard to sell in product conversations.
The best way to persuade a non-believer
is to point them at hard evidence
and for that you need
to do a few things
with your performance data.
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